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vigra/noise_normalization.hxx
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00002 /*                                                                      */
00003 /*               Copyright 1998-2006 by Ullrich Koethe                  */
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00035 
00036 
00037 #ifndef VIGRA_NOISE_NORMALIZATION_HXX
00038 #define VIGRA_NOISE_NORMALIZATION_HXX
00039 
00040 #include "utilities.hxx"
00041 #include "tinyvector.hxx"
00042 #include "stdimage.hxx"
00043 #include "transformimage.hxx"
00044 #include "combineimages.hxx"
00045 #include "localminmax.hxx"
00046 #include "functorexpression.hxx"
00047 #include "numerictraits.hxx"
00048 #include "separableconvolution.hxx"
00049 #include "linear_solve.hxx"
00050 #include "array_vector.hxx"
00051 #include "static_assert.hxx"
00052 #include <algorithm>
00053 
00054 namespace vigra {
00055 
00056 /** \addtogroup NoiseNormalization Noise Normalization
00057     Estimate noise with intensity-dependent variance and transform it into additive Gaussian noise.
00058 */
00059 //@{ 
00060                                     
00061 /********************************************************/
00062 /*                                                      */
00063 /*               NoiseNormalizationOptions              */
00064 /*                                                      */
00065 /********************************************************/
00066 
00067 /** \brief Pass options to one of the noise normalization functions.
00068 
00069     <tt>NoiseNormalizationOptions</tt>  is an argument object that holds various optional
00070     parameters used by the noise normalization functions. If a parameter is not explicitly
00071     set, a suitable default will be used.
00072     
00073     <b> Usage:</b>
00074     
00075         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
00076     Namespace: vigra
00077     
00078     \code
00079     vigra::BImage src(w,h);
00080     std::vector<vigra::TinyVector<double, 2> > result;
00081     
00082     ...
00083     vigra::noiseVarianceEstimation(srcImageRange(src), result, 
00084                                   vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0));
00085     \endcode
00086 */
00087 class NoiseNormalizationOptions
00088 {
00089   public:
00090   
00091         /** Initialize all options with default values.
00092         */
00093     NoiseNormalizationOptions()
00094     : window_radius(6),
00095       cluster_count(10),
00096       noise_estimation_quantile(1.5),
00097       averaging_quantile(0.8),
00098       noise_variance_initial_guess(10.0),
00099       use_gradient(true)
00100     {}
00101 
00102         /** Select the noise estimation algorithm.
00103         
00104             If \a r is <tt>true</tt>, use the gradient-based noise estimator according to F&ouml;rstner (default).
00105             Otherwise, use an algorithm that uses the intensity values directly.
00106         */
00107     NoiseNormalizationOptions & useGradient(bool r)
00108     {
00109         use_gradient = r;
00110         return *this;
00111     }
00112 
00113         /** Set the window radius for a single noise estimate.
00114             Every window of the given size gives raise to one intensity/variance pair.<br>
00115             Default: 6 pixels
00116         */
00117     NoiseNormalizationOptions & windowRadius(unsigned int r)
00118     {
00119         vigra_precondition(r > 0,
00120             "NoiseNormalizationOptions: window radius must be > 0.");
00121         window_radius = r;
00122         return *this;
00123     }
00124 
00125         /** Set the number of clusters for non-parametric noise normalization.
00126             The intensity/variance pairs found are grouped into clusters before the noise
00127             normalization transform is computed.<br>
00128             Default: 10 clusters
00129         */
00130     NoiseNormalizationOptions & clusterCount(unsigned int c)
00131     {
00132         vigra_precondition(c > 0,
00133             "NoiseNormalizationOptions: cluster count must be > 0.");
00134         cluster_count = c;
00135         return *this;
00136     }
00137 
00138         /** Set the quantile for cluster averaging.
00139             After clustering, the cluster center (i.e. average noise variance as a function of the average
00140             intensity in the cluster) is computed using only the cluster members whose estimated variance
00141             is below \a quantile times the maximum variance in the cluster.<br>
00142             Default: 0.8<br>
00143             Precondition: 0 < \a quantile <= 1.0
00144         */
00145     NoiseNormalizationOptions & averagingQuantile(double quantile)
00146     {
00147         vigra_precondition(quantile > 0.0 && quantile <= 1.0,
00148             "NoiseNormalizationOptions: averaging quantile must be between 0 and 1.");
00149         averaging_quantile = quantile;
00150         return *this;
00151     }
00152 
00153         /** Set the operating range of the robust noise estimator.
00154             Intensity changes that are larger than \a quantile times the current estimate of the noise variance
00155             are ignored by the robust noise estimator.<br>
00156             Default: 1.5<br>
00157             Precondition: 0 < \a quantile
00158         */
00159     NoiseNormalizationOptions & noiseEstimationQuantile(double quantile)
00160     {
00161         vigra_precondition(quantile > 0.0,
00162             "NoiseNormalizationOptions: noise estimation quantile must be > 0.");
00163         noise_estimation_quantile = quantile;
00164         return *this;
00165     }
00166 
00167         /** Set the initial estimate of the noise variance.
00168             Robust noise variance estimation is an iterative procedure starting at the given value.<br>
00169             Default: 10.0<br>
00170             Precondition: 0 < \a quess
00171         */
00172     NoiseNormalizationOptions & noiseVarianceInitialGuess(double guess)
00173     {
00174         vigra_precondition(guess > 0.0,
00175             "NoiseNormalizationOptions: noise variance initial guess must be > 0.");
00176         noise_variance_initial_guess = guess;
00177         return *this;
00178     }
00179 
00180     unsigned int window_radius, cluster_count;
00181     double noise_estimation_quantile, averaging_quantile, noise_variance_initial_guess;
00182     bool use_gradient;
00183 };
00184 
00185 //@}
00186 
00187 template <class ArgumentType, class ResultType>
00188 class NonparametricNoiseNormalizationFunctor
00189 {
00190     struct Segment
00191     {
00192         double lower, a, b, shift;
00193     };
00194 
00195     ArrayVector<Segment> segments_;
00196 
00197     template <class T>
00198     double exec(unsigned int k, T t) const
00199     {
00200         if(segments_[k].a == 0.0)
00201         {
00202             return t / VIGRA_CSTD::sqrt(segments_[k].b);
00203         }
00204         else
00205         {
00206             return 2.0 / segments_[k].a * VIGRA_CSTD::sqrt(std::max(0.0, segments_[k].a * t + segments_[k].b));
00207         }
00208     }
00209 
00210   public:
00211     typedef ArgumentType argument_type;
00212     typedef ResultType result_type;
00213 
00214     template <class Vector>
00215     NonparametricNoiseNormalizationFunctor(Vector const & clusters)
00216     : segments_(clusters.size()-1)
00217     {
00218         for(unsigned int k = 0; k<segments_.size(); ++k)
00219         {
00220             segments_[k].lower = clusters[k][0];
00221             segments_[k].a = (clusters[k+1][1] - clusters[k][1]) / (clusters[k+1][0] - clusters[k][0]);
00222             segments_[k].b = clusters[k][1] - segments_[k].a * clusters[k][0];
00223             // FIXME: set a to zero if it is very small
00224             //          - determine what 'very small' means
00225             //          - shouldn't the two formulas (for a == 0, a != 0) be equal in the limit a -> 0 ?
00226 
00227             if(k == 0)
00228             {
00229                 segments_[k].shift = segments_[k].lower - exec(k, segments_[k].lower);
00230             }
00231             else
00232             {
00233                 segments_[k].shift = exec(k-1, segments_[k].lower) - exec(k, segments_[k].lower) + segments_[k-1].shift;
00234             }
00235         }
00236     }
00237 
00238     result_type operator()(argument_type t) const
00239     {
00240         // find the segment
00241         unsigned int k = 0;
00242         for(; k < segments_.size(); ++k)
00243             if(t < segments_[k].lower)
00244                 break;
00245         if(k > 0)
00246             --k;
00247         return detail::RequiresExplicitCast<ResultType>::cast(exec(k, t) + segments_[k].shift);
00248     }
00249 };
00250 
00251 template <class ArgumentType, class ResultType>
00252 class QuadraticNoiseNormalizationFunctor
00253 {
00254     double a, b, c, d, f, o;
00255 
00256     void init(double ia, double ib, double ic, double xmin)
00257     {
00258         a = ia;
00259         b = ib;
00260         c = ic;
00261         d = VIGRA_CSTD::sqrt(VIGRA_CSTD::fabs(c));
00262         if(c > 0.0)
00263         {
00264             o = VIGRA_CSTD::log(VIGRA_CSTD::fabs((2.0*c*xmin + b)/d + 2*VIGRA_CSTD::sqrt(c*sq(xmin) +b*xmin + a)))/d;
00265             f = 0.0;
00266         }
00267         else
00268         {
00269             f = VIGRA_CSTD::sqrt(b*b - 4.0*a*c);
00270             o = -VIGRA_CSTD::asin((2.0*c*xmin+b)/f)/d;
00271         }
00272     }
00273 
00274   public:
00275     typedef ArgumentType argument_type;
00276     typedef ResultType result_type;
00277 
00278     template <class Vector>
00279     QuadraticNoiseNormalizationFunctor(Vector const & clusters)
00280     {
00281         double xmin = NumericTraits<double>::max();
00282         Matrix<double> m(3,3), r(3, 1), l(3, 1);
00283         for(unsigned int k = 0; k<clusters.size(); ++k)
00284         {
00285             l(0,0) = 1.0;
00286             l(1,0) = clusters[k][0];
00287             l(2,0) = sq(clusters[k][0]);
00288             m += outer(l);
00289             r += clusters[k][1]*l;
00290             if(clusters[k][0] < xmin)
00291                 xmin = clusters[k][0];
00292         }
00293 
00294         linearSolve(m, r, l);
00295         init(l(0,0), l(1,0), l(2,0), xmin);
00296     }
00297 
00298     result_type operator()(argument_type t) const
00299     {
00300         double r;
00301         if(c > 0.0)
00302             r = VIGRA_CSTD::log(VIGRA_CSTD::fabs((2.0*c*t + b)/d + 2.0*VIGRA_CSTD::sqrt(c*t*t +b*t + a)))/d-o;
00303         else
00304             r = -VIGRA_CSTD::asin((2.0*c*t+b)/f)/d-o;
00305         return detail::RequiresExplicitCast<ResultType>::cast(r);
00306     }
00307 };
00308 
00309 template <class ArgumentType, class ResultType>
00310 class LinearNoiseNormalizationFunctor
00311 {
00312     double a, b, o;
00313 
00314     void init(double ia, double ib, double xmin)
00315     {
00316         a = ia;
00317         b = ib;
00318         if(b != 0.0)
00319         {
00320             o = xmin - 2.0 / b * VIGRA_CSTD::sqrt(a + b * xmin);
00321         }
00322         else
00323         {
00324             o = xmin - xmin / VIGRA_CSTD::sqrt(a);
00325         }
00326     }
00327 
00328   public:
00329     typedef ArgumentType argument_type;
00330     typedef ResultType result_type;
00331 
00332     template <class Vector>
00333     LinearNoiseNormalizationFunctor(Vector const & clusters)
00334     {
00335         double xmin = NumericTraits<double>::max();
00336         Matrix<double> m(2,2), r(2, 1), l(2, 1);
00337         for(unsigned int k = 0; k<clusters.size(); ++k)
00338         {
00339             l(0,0) = 1.0;
00340             l(1,0) = clusters[k][0];
00341             m += outer(l);
00342             r += clusters[k][1]*l;
00343             if(clusters[k][0] < xmin)
00344                 xmin = clusters[k][0];
00345         }
00346 
00347         linearSolve(m, r, l);
00348         init(l(0,0), l(1,0), xmin);
00349     }
00350 
00351     result_type operator()(argument_type t) const
00352     {
00353         double r;
00354         if(b != 0.0)
00355             r = 2.0 / b * VIGRA_CSTD::sqrt(a + b*t) + o;
00356         else
00357             r =  t / VIGRA_CSTD::sqrt(a) + o;
00358         return detail::RequiresExplicitCast<ResultType>::cast(r);
00359     }
00360 };
00361 
00362 #define VIGRA_NoiseNormalizationFunctor(name, type, size) \
00363 template <class ResultType> \
00364 class name<type, ResultType> \
00365 { \
00366     ResultType lut_[size]; \
00367     \
00368   public: \
00369     typedef type argument_type; \
00370     typedef ResultType result_type; \
00371      \
00372     template <class Vector> \
00373     name(Vector const & clusters) \
00374     { \
00375         name<double, ResultType> f(clusters); \
00376          \
00377         for(unsigned int k = 0; k < size; ++k) \
00378         { \
00379             lut_[k] = f(k); \
00380         } \
00381     } \
00382      \
00383     result_type operator()(argument_type t) const \
00384     { \
00385         return lut_[t]; \
00386     } \
00387 };
00388 
00389 VIGRA_NoiseNormalizationFunctor(NonparametricNoiseNormalizationFunctor, UInt8, 256)
00390 VIGRA_NoiseNormalizationFunctor(NonparametricNoiseNormalizationFunctor, UInt16, 65536)
00391 VIGRA_NoiseNormalizationFunctor(QuadraticNoiseNormalizationFunctor, UInt8, 256)
00392 VIGRA_NoiseNormalizationFunctor(QuadraticNoiseNormalizationFunctor, UInt16, 65536)
00393 VIGRA_NoiseNormalizationFunctor(LinearNoiseNormalizationFunctor, UInt8, 256)
00394 VIGRA_NoiseNormalizationFunctor(LinearNoiseNormalizationFunctor, UInt16, 65536)
00395 
00396 #undef VIGRA_NoiseNormalizationFunctor
00397 
00398 namespace detail {
00399 
00400 template <class SrcIterator, class SrcAcessor,
00401           class GradIterator>
00402 bool
00403 iterativeNoiseEstimationChi2(SrcIterator s, SrcAcessor src, GradIterator g,
00404                          double & mean, double & variance,
00405                          double robustnessThreshold, int windowRadius)
00406 {
00407     double l2 = sq(robustnessThreshold);
00408     double countThreshold = 1.0 - VIGRA_CSTD::exp(-l2);
00409     double f = (1.0 - VIGRA_CSTD::exp(-l2)) / (1.0 - (1.0 + l2)*VIGRA_CSTD::exp(-l2));
00410 
00411     Diff2D ul(-windowRadius, -windowRadius);
00412     int r2 = sq(windowRadius);
00413 
00414     for(int iter=0; iter<100 ; ++iter) // maximum iteration 100 only for terminating
00415                                        // if something is wrong
00416     {
00417         double sum=0.0;
00418         double gsum=0.0;
00419         unsigned int count = 0;
00420         unsigned int tcount = 0;
00421 
00422         SrcIterator siy = s + ul;
00423         GradIterator giy = g + ul;
00424         for(int y=-windowRadius; y <= windowRadius; y++, ++siy.y, ++giy.y)
00425         {
00426             typename SrcIterator::row_iterator six = siy.rowIterator();
00427             typename GradIterator::row_iterator gix = giy.rowIterator();
00428             for(int x=-windowRadius; x <= windowRadius; x++, ++six, ++gix)
00429             {
00430                 if (sq(x) + sq(y) > r2)
00431                     continue;
00432 
00433                 ++tcount;
00434                 if (*gix < l2*variance)
00435                 {
00436                     sum += src(six);
00437                     gsum += *gix;
00438                     ++count;
00439                 }
00440             }
00441         }
00442         if (count==0) // not homogeneous enough
00443             return false;
00444 
00445         double oldvariance = variance;
00446         variance= f * gsum / count;
00447         mean = sum / count;
00448 
00449         if ( closeAtTolerance(oldvariance - variance, 0.0, 1e-10))
00450             return (count >= tcount * countThreshold / 2.0); // sufficiently many valid points
00451     }
00452     return false; // no convergence
00453 }
00454 
00455 template <class SrcIterator, class SrcAcessor,
00456           class GradIterator>
00457 bool
00458 iterativeNoiseEstimationGauss(SrcIterator s, SrcAcessor src, GradIterator,
00459                          double & mean, double & variance,
00460                          double robustnessThreshold, int windowRadius)
00461 {
00462     double l2 = sq(robustnessThreshold);
00463     double countThreshold = erf(VIGRA_CSTD::sqrt(0.5 * l2));
00464     double f = countThreshold / (countThreshold - VIGRA_CSTD::sqrt(2.0/M_PI*l2)*VIGRA_CSTD::exp(-l2/2.0));
00465 
00466     mean = src(s);
00467 
00468     Diff2D ul(-windowRadius, -windowRadius);
00469     int r2 = sq(windowRadius);
00470 
00471     for(int iter=0; iter<100 ; ++iter) // maximum iteration 100 only for terminating
00472                                        // if something is wrong
00473     {
00474         double sum = 0.0;
00475         double sum2 = 0.0;
00476         unsigned int count = 0;
00477         unsigned int tcount = 0;
00478 
00479         SrcIterator siy = s + ul;
00480         for(int y=-windowRadius; y <= windowRadius; y++, ++siy.y)
00481         {
00482             typename SrcIterator::row_iterator six = siy.rowIterator();
00483             for(int x=-windowRadius; x <= windowRadius; x++, ++six)
00484             {
00485                 if (sq(x) + sq(y) > r2)
00486                     continue;
00487 
00488                 ++tcount;
00489                 if (sq(src(six) - mean) < l2*variance)
00490                 {
00491                     sum += src(six);
00492                     sum2 += sq(src(six));
00493                     ++count;
00494                 }
00495             }
00496         }
00497         if (count==0) // not homogeneous enough
00498             return false;
00499 
00500         double oldmean = mean;
00501         double oldvariance = variance;
00502         mean = sum / count;
00503         variance= f * (sum2 / count - sq(mean));
00504 
00505         if ( closeAtTolerance(oldmean - mean, 0.0, 1e-10) &&
00506              closeAtTolerance(oldvariance - variance, 0.0, 1e-10))
00507             return (count >= tcount * countThreshold / 2.0); // sufficiently many valid points
00508     }
00509     return false; // no convergence
00510 }
00511 
00512 
00513 template <class SrcIterator, class SrcAccessor,
00514           class DestIterator, class DestAccessor>
00515 void
00516 symmetricDifferenceSquaredMagnitude(
00517      SrcIterator sul, SrcIterator slr, SrcAccessor src,
00518      DestIterator dul, DestAccessor dest)
00519 {
00520     using namespace functor;
00521     int w = slr.x - sul.x;
00522     int h = slr.y - sul.y;
00523 
00524     typedef typename NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
00525     typedef BasicImage<TmpType> TmpImage;
00526 
00527     Kernel1D<double> mask;
00528     mask.initSymmetricGradient();
00529     mask.setBorderTreatment(BORDER_TREATMENT_REFLECT);
00530 
00531     TmpImage dx(w, h), dy(w, h);
00532     separableConvolveX(srcIterRange(sul, slr, src), destImage(dx),  kernel1d(mask));
00533     separableConvolveY(srcIterRange(sul, slr, src), destImage(dy),  kernel1d(mask));
00534     combineTwoImages(srcImageRange(dx), srcImage(dy), destIter(dul, dest), Arg1()*Arg1() + Arg2()*Arg2());
00535 }
00536 
00537 template <class SrcIterator, class SrcAccessor,
00538           class DestIterator, class DestAccessor>
00539 void
00540 findHomogeneousRegionsFoerstner(
00541      SrcIterator sul, SrcIterator slr, SrcAccessor src,
00542      DestIterator dul, DestAccessor dest,
00543      unsigned int windowRadius = 6, double homogeneityThreshold = 40.0)
00544 {
00545     using namespace vigra::functor;
00546     int w = slr.x - sul.x;
00547     int h = slr.y - sul.y;
00548 
00549     typedef typename NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
00550     typedef BasicImage<TmpType> TmpImage;
00551 
00552     BImage btmp(w, h);
00553     transformImage(srcIterRange(sul, slr, src), destImage(btmp),
00554                     ifThenElse(Arg1() <= Param(homogeneityThreshold), Param(1), Param(0)));
00555 
00556     // Erosion
00557     discErosion(srcImageRange(btmp), destIter(dul, dest), windowRadius);
00558 }
00559 
00560 template <class SrcIterator, class SrcAccessor,
00561           class DestIterator, class DestAccessor>
00562 void
00563 findHomogeneousRegions(
00564      SrcIterator sul, SrcIterator slr, SrcAccessor src,
00565      DestIterator dul, DestAccessor dest)
00566 {
00567     localMinima(sul, slr, src, dul, dest);
00568 }
00569 
00570 template <class Vector1, class Vector2>
00571 void noiseVarianceListMedianCut(Vector1 const & noise, Vector2 & clusters,
00572                                 unsigned int maxClusterCount)
00573 {
00574     typedef typename Vector2::value_type Result;
00575 
00576     clusters.push_back(Result(0, noise.size()));
00577 
00578     while(clusters.size() <= maxClusterCount)
00579     {
00580         // find biggest cluster
00581         unsigned int kMax = 0;
00582         double diffMax = 0.0;
00583         for(unsigned int k=0; k < clusters.size(); ++k)
00584         {
00585             double diff = noise[clusters[k][1]-1][0] - noise[clusters[k][0]][0];
00586             if(diff > diffMax)
00587             {
00588                 diffMax = diff;
00589                 kMax = k;
00590             }
00591         }
00592 
00593         if(diffMax == 0.0)
00594             return; // all clusters have only one value
00595 
00596         unsigned int k1 = clusters[kMax][0],
00597                      k2 = clusters[kMax][1];
00598         unsigned int kSplit = k1 + (k2 - k1) / 2;
00599         clusters[kMax][1] = kSplit;
00600         clusters.push_back(Result(kSplit, k2));
00601     }
00602 }
00603 
00604 struct SortNoiseByMean
00605 {
00606     template <class T>
00607     bool operator()(T const & l, T const & r) const
00608     {
00609         return l[0] < r[0];
00610     }
00611 };
00612 
00613 struct SortNoiseByVariance
00614 {
00615     template <class T>
00616     bool operator()(T const & l, T const & r) const
00617     {
00618         return l[1] < r[1];
00619     }
00620 };
00621 
00622 template <class Vector1, class Vector2, class Vector3>
00623 void noiseVarianceClusterAveraging(Vector1 & noise, Vector2 & clusters,
00624                                    Vector3 & result, double quantile)
00625 {
00626     typedef typename Vector1::iterator Iter;
00627     typedef typename Vector3::value_type Result;
00628 
00629     for(unsigned int k=0; k<clusters.size(); ++k)
00630     {
00631         Iter i1 = noise.begin() + clusters[k][0];
00632         Iter i2 = noise.begin() + clusters[k][1];
00633 
00634         std::sort(i1, i2, SortNoiseByVariance());
00635 
00636         std::size_t size = static_cast<std::size_t>(VIGRA_CSTD::ceil(quantile*(i2 - i1)));
00637         if(static_cast<std::size_t>(i2 - i1) < size)
00638             size = i2 - i1;
00639         if(size < 1)
00640             size = 1;
00641         i2 = i1 + size;
00642 
00643         double mean = 0.0,
00644                variance = 0.0;
00645         for(; i1 < i2; ++i1)
00646         {
00647             mean += (*i1)[0];
00648             variance += (*i1)[1];
00649         }
00650 
00651         result.push_back(Result(mean / size, variance / size));
00652     }
00653 }
00654 
00655 template <class SrcIterator, class SrcAccessor, class BackInsertable>
00656 void noiseVarianceEstimationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00657                            BackInsertable & result,
00658                            NoiseNormalizationOptions const & options)
00659 {
00660     typedef typename BackInsertable::value_type ResultType;
00661 
00662     unsigned int w = slr.x - sul.x;
00663     unsigned int h = slr.y - sul.y;
00664 
00665     typedef typename NumericTraits<typename SrcAccessor::value_type>::RealPromote TmpType;
00666     typedef BasicImage<TmpType> TmpImage;
00667 
00668     TmpImage gradient(w, h);
00669     symmetricDifferenceSquaredMagnitude(sul, slr, src, gradient.upperLeft(), gradient.accessor());
00670 
00671     BImage homogeneous(w, h);
00672     findHomogeneousRegions(gradient.upperLeft(), gradient.lowerRight(), gradient.accessor(),
00673                                    homogeneous.upperLeft(), homogeneous.accessor());
00674 
00675     // Generate noise of each of the remaining pixels == centers of homogenous areas (border is not used)
00676     unsigned int windowRadius = options.window_radius;
00677     for(unsigned int y=windowRadius; y<h-windowRadius; ++y)
00678     {
00679         for(unsigned int x=windowRadius; x<w-windowRadius; ++x)
00680         {
00681             if (! homogeneous(x, y))
00682                 continue;
00683 
00684             Diff2D center(x, y);
00685             double mean = 0.0, variance = options.noise_variance_initial_guess;
00686 
00687             bool success;
00688 
00689             if(options.use_gradient)
00690             {
00691                 success = iterativeNoiseEstimationChi2(sul + center, src,
00692                               gradient.upperLeft() + center, mean, variance,
00693                               options.noise_estimation_quantile, windowRadius);
00694             }
00695             else
00696             {
00697                 success = iterativeNoiseEstimationGauss(sul + center, src,
00698                               gradient.upperLeft() + center, mean, variance,
00699                               options.noise_estimation_quantile, windowRadius);
00700             }
00701             if (success)
00702             {
00703                 result.push_back(ResultType(mean, variance));
00704             }
00705         }
00706     }
00707 }
00708 
00709 template <class Vector, class BackInsertable>
00710 void noiseVarianceClusteringImpl(Vector & noise, BackInsertable & result,
00711                            unsigned int clusterCount, double quantile)
00712 {
00713     std::sort(noise.begin(), noise.end(), detail::SortNoiseByMean());
00714 
00715     ArrayVector<TinyVector<unsigned int, 2> > clusters;
00716     detail::noiseVarianceListMedianCut(noise, clusters, clusterCount);
00717 
00718     std::sort(clusters.begin(), clusters.end(), detail::SortNoiseByMean());
00719 
00720     detail::noiseVarianceClusterAveraging(noise, clusters, result, quantile);
00721 }
00722 
00723 template <class Functor,
00724           class SrcIterator, class SrcAccessor,
00725           class DestIterator, class DestAccessor>
00726 bool
00727 noiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00728                        DestIterator dul, DestAccessor dest,
00729                        NoiseNormalizationOptions const & options)
00730 {
00731     ArrayVector<TinyVector<double, 2> > noiseData;
00732     noiseVarianceEstimationImpl(sul, slr, src, noiseData, options);
00733 
00734     if(noiseData.size() < 10)
00735         return false;
00736 
00737     ArrayVector<TinyVector<double, 2> > noiseClusters;
00738 
00739     noiseVarianceClusteringImpl(noiseData, noiseClusters,
00740                                   options.cluster_count, options.averaging_quantile);
00741 
00742     transformImage(sul, slr, src, dul, dest, Functor(noiseClusters));
00743 
00744     return true;
00745 }
00746 
00747 template <class SrcIterator, class SrcAccessor,
00748           class DestIterator, class DestAccessor>
00749 bool
00750 nonparametricNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00751                                     DestIterator dul, DestAccessor dest,
00752                                     NoiseNormalizationOptions const & options,
00753                                     VigraTrueType /* isScalar */)
00754 {
00755     typedef typename SrcAccessor::value_type SrcType;
00756     typedef typename DestAccessor::value_type DestType;
00757     return noiseNormalizationImpl<NonparametricNoiseNormalizationFunctor<SrcType, DestType> >
00758                                                          (sul, slr, src, dul, dest, options);
00759 }
00760 
00761 template <class SrcIterator, class SrcAccessor,
00762           class DestIterator, class DestAccessor>
00763 bool
00764 nonparametricNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00765                               DestIterator dul, DestAccessor dest,
00766                               NoiseNormalizationOptions const & options,
00767                               VigraFalseType /* isScalar */)
00768 {
00769     int bands = src.size(sul);
00770     for(int b=0; b<bands; ++b)
00771     {
00772         VectorElementAccessor<SrcAccessor> sband(b, src);
00773         VectorElementAccessor<DestAccessor> dband(b, dest);
00774         typedef typename VectorElementAccessor<SrcAccessor>::value_type SrcType;
00775         typedef typename VectorElementAccessor<DestAccessor>::value_type DestType;
00776 
00777         if(!noiseNormalizationImpl<NonparametricNoiseNormalizationFunctor<SrcType, DestType> >
00778                                                            (sul, slr, sband, dul, dband, options))
00779             return false;
00780     }
00781     return true;
00782 }
00783 
00784 template <class SrcIterator, class SrcAccessor,
00785           class DestIterator, class DestAccessor>
00786 bool
00787 quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00788                                     DestIterator dul, DestAccessor dest,
00789                                     NoiseNormalizationOptions const & options,
00790                                     VigraTrueType /* isScalar */)
00791 {
00792     typedef typename SrcAccessor::value_type SrcType;
00793     typedef typename DestAccessor::value_type DestType;
00794     return noiseNormalizationImpl<QuadraticNoiseNormalizationFunctor<SrcType, DestType> >
00795                                                          (sul, slr, src, dul, dest, options);
00796 }
00797 
00798 template <class SrcIterator, class SrcAccessor,
00799           class DestIterator, class DestAccessor>
00800 bool
00801 quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00802                               DestIterator dul, DestAccessor dest,
00803                               NoiseNormalizationOptions const & options,
00804                               VigraFalseType /* isScalar */)
00805 {
00806     int bands = src.size(sul);
00807     for(int b=0; b<bands; ++b)
00808     {
00809         VectorElementAccessor<SrcAccessor> sband(b, src);
00810         VectorElementAccessor<DestAccessor> dband(b, dest);
00811         typedef typename VectorElementAccessor<SrcAccessor>::value_type SrcType;
00812         typedef typename VectorElementAccessor<DestAccessor>::value_type DestType;
00813 
00814         if(!noiseNormalizationImpl<QuadraticNoiseNormalizationFunctor<SrcType, DestType> >
00815                                                            (sul, slr, sband, dul, dband, options))
00816             return false;
00817     }
00818     return true;
00819 }
00820 
00821 template <class SrcIterator, class SrcAccessor,
00822           class DestIterator, class DestAccessor>
00823 void
00824 quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00825                               DestIterator dul, DestAccessor dest,
00826                               double a0, double a1, double a2,
00827                               VigraTrueType /* isScalar */)
00828 {
00829     ArrayVector<TinyVector<double, 2> > noiseClusters;
00830     noiseClusters.push_back(TinyVector<double, 2>(0.0, a0));
00831     noiseClusters.push_back(TinyVector<double, 2>(1.0, a0 + a1 + a2));
00832     noiseClusters.push_back(TinyVector<double, 2>(2.0, a0 + 2.0*a1 + 4.0*a2));
00833     transformImage(sul, slr, src, dul, dest,
00834                    QuadraticNoiseNormalizationFunctor<typename SrcAccessor::value_type,
00835                                                    typename DestAccessor::value_type>(noiseClusters));
00836 }
00837 
00838 template <class SrcIterator, class SrcAccessor,
00839           class DestIterator, class DestAccessor>
00840 void
00841 quadraticNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00842                               DestIterator dul, DestAccessor dest,
00843                               double a0, double a1, double a2,
00844                               VigraFalseType /* isScalar */)
00845 {
00846     int bands = src.size(sul);
00847     for(int b=0; b<bands; ++b)
00848     {
00849         VectorElementAccessor<SrcAccessor> sband(b, src);
00850         VectorElementAccessor<DestAccessor> dband(b, dest);
00851         quadraticNoiseNormalizationImpl(sul, slr, sband, dul, dband, a0, a1, a2, VigraTrueType());
00852     }
00853 }
00854 
00855 template <class SrcIterator, class SrcAccessor,
00856           class DestIterator, class DestAccessor>
00857 bool
00858 linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00859                                     DestIterator dul, DestAccessor dest,
00860                                     NoiseNormalizationOptions const & options,
00861                                     VigraTrueType /* isScalar */)
00862 {
00863     typedef typename SrcAccessor::value_type SrcType;
00864     typedef typename DestAccessor::value_type DestType;
00865     return noiseNormalizationImpl<LinearNoiseNormalizationFunctor<SrcType, DestType> >
00866                                                          (sul, slr, src, dul, dest, options);
00867 }
00868 
00869 template <class SrcIterator, class SrcAccessor,
00870           class DestIterator, class DestAccessor>
00871 bool
00872 linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00873                               DestIterator dul, DestAccessor dest,
00874                               NoiseNormalizationOptions const & options,
00875                               VigraFalseType /* isScalar */)
00876 {
00877     int bands = src.size(sul);
00878     for(int b=0; b<bands; ++b)
00879     {
00880         VectorElementAccessor<SrcAccessor> sband(b, src);
00881         VectorElementAccessor<DestAccessor> dband(b, dest);
00882         typedef typename VectorElementAccessor<SrcAccessor>::value_type SrcType;
00883         typedef typename VectorElementAccessor<DestAccessor>::value_type DestType;
00884 
00885         if(!noiseNormalizationImpl<LinearNoiseNormalizationFunctor<SrcType, DestType> >
00886                                                            (sul, slr, sband, dul, dband, options))
00887             return false;
00888     }
00889     return true;
00890 }
00891 
00892 template <class SrcIterator, class SrcAccessor,
00893           class DestIterator, class DestAccessor>
00894 void
00895 linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00896                               DestIterator dul, DestAccessor dest,
00897                               double a0, double a1,
00898                               VigraTrueType /* isScalar */)
00899 {
00900     ArrayVector<TinyVector<double, 2> > noiseClusters;
00901     noiseClusters.push_back(TinyVector<double, 2>(0.0, a0));
00902     noiseClusters.push_back(TinyVector<double, 2>(1.0, a0 + a1));
00903     transformImage(sul, slr, src, dul, dest,
00904                    LinearNoiseNormalizationFunctor<typename SrcAccessor::value_type,
00905                                                    typename DestAccessor::value_type>(noiseClusters));
00906 }
00907 
00908 template <class SrcIterator, class SrcAccessor,
00909           class DestIterator, class DestAccessor>
00910 void
00911 linearNoiseNormalizationImpl(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00912                               DestIterator dul, DestAccessor dest,
00913                               double a0, double a1,
00914                               VigraFalseType /* isScalar */)
00915 {
00916     int bands = src.size(sul);
00917     for(int b=0; b<bands; ++b)
00918     {
00919         VectorElementAccessor<SrcAccessor> sband(b, src);
00920         VectorElementAccessor<DestAccessor> dband(b, dest);
00921         linearNoiseNormalizationImpl(sul, slr, sband, dul, dband, a0, a1, VigraTrueType());
00922     }
00923 }
00924 
00925 } // namespace detail
00926 
00927 template <bool P>
00928 struct noiseVarianceEstimation_can_only_work_on_scalar_images
00929 : vigra::staticAssert::AssertBool<P>
00930 {};
00931 
00932 /** \addtogroup NoiseNormalization Noise Normalization
00933     Estimate noise with intensity-dependent variance and transform it into additive Gaussian noise.
00934 */
00935 //@{ 
00936                                     
00937 /********************************************************/
00938 /*                                                      */
00939 /*                noiseVarianceEstimation               */
00940 /*                                                      */
00941 /********************************************************/
00942 
00943 /** \brief Determine the noise variance as a function of the image intensity.
00944 
00945     This operator applies an algorithm described in 
00946     
00947     W. F&ouml;rstner: <i>"Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images"</i>, 
00948     Proc. Summer School on Data Analysis and the Statistical Foundations of Geomatics, 
00949     Lecture Notes in Earth Science, Berlin: Springer, 1999
00950     
00951     in order to estimate the noise variance as a function of the image intensity in a robust way,
00952     i.e. so that intensity changes due to edges do not bias the estimate. The source value type 
00953     (<TT>SrcAccessor::value_type</TT>) must be a scalar type which is convertible to <tt>double</tt>.
00954     The result is written into the \a result sequence, whose <tt>value_type</tt> must be constructible 
00955     from two <tt>double</tt> values. The following options can be set via the \a options object 
00956     (see \ref vigra::NoiseNormalizationOptions for details):<br><br>
00957     
00958     <tt>useGradient</tt>, <tt>windowRadius</tt>, <tt>noiseEstimationQuantile</tt>, <tt>noiseVarianceInitialGuess</tt>
00959     
00960     <b> Declarations:</b>
00961     
00962     pass arguments explicitly:
00963     \code
00964     namespace vigra {
00965         template <class SrcIterator, class SrcAccessor, class BackInsertable>
00966         void noiseVarianceEstimation(SrcIterator sul, SrcIterator slr, SrcAccessor src,
00967                                      BackInsertable & result,
00968                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
00969     }
00970     \endcode
00971     
00972     use argument objects in conjunction with \ref ArgumentObjectFactories :
00973     \code
00974     namespace vigra {
00975         template <class SrcIterator, class SrcAccessor, class BackInsertable>
00976         void noiseVarianceEstimation(triple<SrcIterator, SrcIterator, SrcAccessor> src,
00977                                      BackInsertable & result,
00978                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
00979     }
00980     \endcode
00981     
00982     <b> Usage:</b>
00983     
00984         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
00985     Namespace: vigra
00986     
00987     \code
00988     vigra::BImage src(w,h);
00989     std::vector<vigra::TinyVector<double, 2> > result;
00990     
00991     ...
00992     vigra::noiseVarianceEstimation(srcImageRange(src), result, 
00993                                   vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0));
00994     
00995     // print the intensity / variance pairs found
00996     for(int k=0; k<result.size(); ++k)
00997         std::cout << "Intensity: " << result[k][0] << ", estimated variance: " << result[k][1] << std::endl;
00998     \endcode
00999 
01000     <b> Required Interface:</b>
01001     
01002     \code
01003     SrcIterator upperleft, lowerright;
01004     SrcAccessor src;
01005     
01006     typedef SrcAccessor::value_type SrcType;
01007     typedef NumericTraits<SrcType>::isScalar isScalar;
01008     assert(isScalar::asBool == true);
01009     
01010     double value = src(uperleft);
01011     
01012     BackInsertable result;
01013     typedef BackInsertable::value_type ResultType;    
01014     double intensity, variance;
01015     result.push_back(ResultType(intensity, variance));
01016     \endcode
01017 */
01018 doxygen_overloaded_function(template <...> void noiseVarianceEstimation)
01019 
01020 template <class SrcIterator, class SrcAccessor, class BackInsertable>
01021 inline
01022 void noiseVarianceEstimation(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01023                            BackInsertable & result,
01024                            NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01025 {
01026     typedef typename BackInsertable::value_type ResultType;
01027     typedef typename SrcAccessor::value_type SrcType;
01028     typedef typename NumericTraits<SrcType>::isScalar isScalar;
01029 
01030     VIGRA_STATIC_ASSERT((
01031         noiseVarianceEstimation_can_only_work_on_scalar_images<(isScalar::asBool)>));
01032 
01033     detail::noiseVarianceEstimationImpl(sul, slr, src, result, options);
01034 }
01035 
01036 template <class SrcIterator, class SrcAccessor, class BackInsertable>
01037 inline
01038 void noiseVarianceEstimation(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01039                            BackInsertable & result,
01040                            NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01041 {
01042     noiseVarianceEstimation(src.first, src.second, src.third, result, options);
01043 }
01044 
01045 /********************************************************/
01046 /*                                                      */
01047 /*                noiseVarianceClustering               */
01048 /*                                                      */
01049 /********************************************************/
01050 
01051 /** \brief Determine the noise variance as a function of the image intensity and cluster the results.
01052 
01053     This operator first calls \ref noiseVarianceEstimation() to obtain a sequence of intensity/variance pairs,
01054     which are then clustered using the median cut algorithm. Then the cluster centers (i.e. average variance vs.
01055     average intensity) are determined and returned in the \a result sequence.
01056     
01057     In addition to the options valid for \ref noiseVarianceEstimation(), the following options can be set via 
01058     the \a options object (see \ref vigra::NoiseNormalizationOptions for details):<br><br>
01059     
01060     <tt>clusterCount</tt>, <tt>averagingQuantile</tt>
01061     
01062     <b> Declarations:</b>
01063     
01064     pass arguments explicitly:
01065     \code
01066     namespace vigra {
01067         template <class SrcIterator, class SrcAccessor, class BackInsertable>
01068         void noiseVarianceClustering(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01069                                 BackInsertable & result,
01070                                 NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01071     }
01072     \endcode
01073     
01074     use argument objects in conjunction with \ref ArgumentObjectFactories :
01075     \code
01076     namespace vigra {
01077         template <class SrcIterator, class SrcAccessor, class BackInsertable>
01078         void noiseVarianceClustering(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01079                                 BackInsertable & result,
01080                                 NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01081     }
01082     \endcode
01083     
01084     <b> Usage:</b>
01085     
01086         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
01087     Namespace: vigra
01088     
01089     \code
01090     vigra::BImage src(w,h);
01091     std::vector<vigra::TinyVector<double, 2> > result;
01092     
01093     ...
01094     vigra::noiseVarianceClustering(srcImageRange(src), result, 
01095                                   vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
01096                                   clusterCount(15));
01097     
01098     // print the intensity / variance pairs representing the cluster centers
01099     for(int k=0; k<result.size(); ++k)
01100         std::cout << "Cluster: " << k << ", intensity: " << result[k][0] << ", estimated variance: " << result[k][1] << std::endl;
01101     \endcode
01102 
01103     <b> Required Interface:</b>
01104     
01105     same as \ref noiseVarianceEstimation()
01106 */
01107 doxygen_overloaded_function(template <...> void noiseVarianceClustering)
01108 
01109 template <class SrcIterator, class SrcAccessor, class BackInsertable>
01110 inline
01111 void noiseVarianceClustering(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01112                            BackInsertable & result,
01113                            NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01114 {
01115     ArrayVector<TinyVector<double, 2> > variance;
01116     noiseVarianceEstimation(sul, slr, src, variance, options);
01117     detail::noiseVarianceClusteringImpl(variance, result, options.cluster_count, options.averaging_quantile);
01118 }
01119 
01120 template <class SrcIterator, class SrcAccessor, class BackInsertable>
01121 inline
01122 void noiseVarianceClustering(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01123                            BackInsertable & result,
01124                            NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01125 {
01126     noiseVarianceClustering(src.first, src.second, src.third, result, options);
01127 }
01128 
01129 /********************************************************/
01130 /*                                                      */
01131 /*             nonparametricNoiseNormalization          */
01132 /*                                                      */
01133 /********************************************************/
01134 
01135 /** \brief Noise normalization by means of an estimated non-parametric noise model.
01136 
01137     The original image is assumed to be corrupted by noise whose variance depends on the intensity in an unknown way.
01138     The present functions first calls \ref noiseVarianceClustering() to obtain a sequence of intensity/variance pairs
01139     (cluster centers) which estimate this dependency. The cluster centers are connected into a piecewise linear 
01140     function which is the inverted according to the formula derived in 
01141     
01142     W. F&ouml;rstner: <i>"Image Preprocessing for Feature Extraction in Digital Intensity, Color and Range Images"</i>, 
01143     Proc. Summer School on Data Analysis and the Statistical Foundations of Geomatics, 
01144     Lecture Notes in Earth Science, Berlin: Springer, 1999
01145 
01146     The inverted formula defines a pixel-wise intensity transformation whose application turns the original image
01147     into one that is corrupted by additive Gaussian noise with unit variance. Most subsequent algorithms will be able
01148     to handle this type of noise much better than the original noise.
01149     
01150     RGB and other multiband images will be processed one band at a time. The function returns <tt>true</tt> on success.
01151     Noise normalization will fail if the original image does not contain sufficiently homogeneous regions that
01152     allow robust estimation of the noise variance.
01153     
01154     The \a options object may use all options described in \ref vigra::NoiseNormalizationOptions.
01155     
01156     <b> Declarations:</b>
01157     
01158     pass arguments explicitly:
01159     \code
01160     namespace vigra {
01161         template <class SrcIterator, class SrcAccessor,
01162                   class DestIterator, class DestAccessor>
01163         bool nonparametricNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01164                                              DestIterator dul, DestAccessor dest,
01165                                              NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01166     }
01167     \endcode
01168     
01169     use argument objects in conjunction with \ref ArgumentObjectFactories :
01170     \code
01171     namespace vigra {
01172         template <class SrcIterator, class SrcAccessor,
01173                   class DestIterator, class DestAccessor>
01174         bool nonparametricNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01175                                              pair<DestIterator, DestAccessor> dest,
01176                                              NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01177     }
01178     \endcode
01179     
01180     <b> Usage:</b>
01181     
01182         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
01183     Namespace: vigra
01184     
01185     \code
01186     vigra::BRGBImage src(w,h), dest(w, h);
01187     
01188     ...
01189     vigra::nonparametricNoiseNormalization(srcImageRange(src), destImage(dest), 
01190                                            vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
01191                                            clusterCount(15));
01192     \endcode
01193 
01194     <b> Required Interface:</b>
01195     
01196     same as \ref noiseVarianceEstimation()
01197 */
01198 doxygen_overloaded_function(template <...> bool nonparametricNoiseNormalization)
01199 
01200 template <class SrcIterator, class SrcAccessor,
01201           class DestIterator, class DestAccessor>
01202 inline bool
01203 nonparametricNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01204                                 DestIterator dul, DestAccessor dest,
01205                                 NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01206 {
01207     typedef typename SrcAccessor::value_type SrcType;
01208 
01209     return detail::nonparametricNoiseNormalizationImpl(sul, slr, src, dul, dest, options,
01210                                          typename NumericTraits<SrcType>::isScalar());
01211 }
01212 
01213 template <class SrcIterator, class SrcAccessor,
01214           class DestIterator, class DestAccessor>
01215 inline
01216 bool nonparametricNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01217                                      pair<DestIterator, DestAccessor> dest,
01218                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01219 {
01220     return nonparametricNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, options);
01221 }
01222 
01223 /********************************************************/
01224 /*                                                      */
01225 /*               quadraticNoiseNormalization            */
01226 /*                                                      */
01227 /********************************************************/
01228 
01229 /** \brief Noise normalization by means of an estimated quadratic noise model.
01230 
01231     This function works in the same way as \ref nonparametricNoiseNormalization() with the exception of the 
01232     model for the dependency between intensity and noise variance: it assumes that this dependency is a 
01233     quadratic function rather than a piecewise linear function. If the quadratic model is applicable, it leads
01234     to a somewhat smoother transformation.
01235     
01236     <b> Declarations:</b>
01237     
01238     pass arguments explicitly:
01239     \code
01240     namespace vigra {
01241         template <class SrcIterator, class SrcAccessor,
01242                   class DestIterator, class DestAccessor>
01243         bool quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01244                                          DestIterator dul, DestAccessor dest,
01245                                          NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01246     }
01247     \endcode
01248     
01249     use argument objects in conjunction with \ref ArgumentObjectFactories :
01250     \code
01251     namespace vigra {
01252         template <class SrcIterator, class SrcAccessor,
01253                   class DestIterator, class DestAccessor>
01254         bool quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01255                                          pair<DestIterator, DestAccessor> dest,
01256                                          NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01257     }
01258     \endcode
01259     
01260     <b> Usage:</b>
01261     
01262         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
01263     Namespace: vigra
01264     
01265     \code
01266     vigra::BRGBImage src(w,h), dest(w, h);
01267     
01268     ...
01269     vigra::quadraticNoiseNormalization(srcImageRange(src), destImage(dest), 
01270                                        vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
01271                                        clusterCount(15));
01272     \endcode
01273 
01274     <b> Required Interface:</b>
01275     
01276     same as \ref noiseVarianceEstimation()
01277 */
01278 doxygen_overloaded_function(template <...> bool quadraticNoiseNormalization)
01279 
01280 template <class SrcIterator, class SrcAccessor,
01281           class DestIterator, class DestAccessor>
01282 inline bool
01283 quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01284                                 DestIterator dul, DestAccessor dest,
01285                                 NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01286 {
01287     typedef typename SrcAccessor::value_type SrcType;
01288 
01289     return detail::quadraticNoiseNormalizationImpl(sul, slr, src, dul, dest, options,
01290                                          typename NumericTraits<SrcType>::isScalar());
01291 }
01292 
01293 template <class SrcIterator, class SrcAccessor,
01294           class DestIterator, class DestAccessor>
01295 inline
01296 bool quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01297                                      pair<DestIterator, DestAccessor> dest,
01298                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01299 {
01300     return quadraticNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, options);
01301 }
01302 
01303 /********************************************************/
01304 /*                                                      */
01305 /*               quadraticNoiseNormalization            */
01306 /*                                                      */
01307 /********************************************************/
01308 
01309 /** \brief Noise normalization by means of a given quadratic noise model.
01310 
01311     This function works similar to \ref nonparametricNoiseNormalization() with the exception that the 
01312     functional dependency of the noise variance from the intensity is given (by a quadratic function)
01313     rather than estimated:
01314     
01315     \code
01316     variance = a0 + a1 * intensity + a2 * sq(intensity)
01317     \endcode
01318     
01319     <b> Declarations:</b>
01320     
01321     pass arguments explicitly:
01322     \code
01323     namespace vigra {
01324         template <class SrcIterator, class SrcAccessor,
01325                   class DestIterator, class DestAccessor>
01326         void quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01327                                          DestIterator dul, DestAccessor dest,
01328                                          double a0, double a1, double a2);
01329     }
01330     \endcode
01331     
01332     use argument objects in conjunction with \ref ArgumentObjectFactories :
01333     \code
01334     namespace vigra {
01335         template <class SrcIterator, class SrcAccessor,
01336                   class DestIterator, class DestAccessor>
01337         void quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01338                                         pair<DestIterator, DestAccessor> dest,
01339                                         double a0, double a1, double a2);
01340     }
01341     \endcode
01342     
01343     <b> Usage:</b>
01344     
01345         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
01346     Namespace: vigra
01347     
01348     \code
01349     vigra::BRGBImage src(w,h), dest(w, h);
01350     
01351     ...
01352     vigra::quadraticNoiseNormalization(srcImageRange(src), destImage(dest), 
01353                                        100, 0.02, 1e-6);
01354     \endcode
01355 
01356     <b> Required Interface:</b>
01357     
01358     The source value type must be convertible to <tt>double</tt> or must be a vector whose elements 
01359     are convertible to <tt>double</tt>. Likewise, the destination type must be assignable from <tt>double</tt>
01360     or a vector whose elements are assignable from <tt>double</tt>.
01361 */
01362 doxygen_overloaded_function(template <...> void quadraticNoiseNormalization)
01363 
01364 template <class SrcIterator, class SrcAccessor,
01365           class DestIterator, class DestAccessor>
01366 inline void
01367 quadraticNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01368                             DestIterator dul, DestAccessor dest,
01369                             double a0, double a1, double a2)
01370 {
01371     typedef typename SrcAccessor::value_type SrcType;
01372 
01373     detail::quadraticNoiseNormalizationImpl(sul, slr, src, dul, dest, a0, a1, a2,
01374                                          typename NumericTraits<SrcType>::isScalar());
01375 }
01376 
01377 template <class SrcIterator, class SrcAccessor,
01378           class DestIterator, class DestAccessor>
01379 inline
01380 void quadraticNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01381                                  pair<DestIterator, DestAccessor> dest,
01382                                  double a0, double a1, double a2)
01383 {
01384     quadraticNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, a0, a1, a2);
01385 }
01386 
01387 /********************************************************/
01388 /*                                                      */
01389 /*                linearNoiseNormalization              */
01390 /*                                                      */
01391 /********************************************************/
01392 
01393 /** \brief Noise normalization by means of an estimated linear noise model.
01394 
01395     This function works in the same way as \ref nonparametricNoiseNormalization() with the exception of the 
01396     model for the dependency between intensity and noise variance: it assumes that this dependency is a 
01397     linear function rather than a piecewise linear function. If the linear model is applicable, it leads
01398     to a very simple transformation which is similar to the familiar gamma correction.
01399     
01400     <b> Declarations:</b>
01401     
01402     pass arguments explicitly:
01403     \code
01404     namespace vigra {
01405         template <class SrcIterator, class SrcAccessor,
01406                 class DestIterator, class DestAccessor>
01407         bool linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01408                                       DestIterator dul, DestAccessor dest,
01409                                       NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01410     }
01411     \endcode
01412     
01413     use argument objects in conjunction with \ref ArgumentObjectFactories :
01414     \code
01415     namespace vigra {
01416         template <class SrcIterator, class SrcAccessor,
01417                   class DestIterator, class DestAccessor>
01418         bool linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01419                                       pair<DestIterator, DestAccessor> dest,
01420                                       NoiseNormalizationOptions const & options = NoiseNormalizationOptions());
01421     }
01422     \endcode
01423     
01424     <b> Usage:</b>
01425     
01426         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
01427     Namespace: vigra
01428     
01429     \code
01430     vigra::BRGBImage src(w,h), dest(w, h);
01431     
01432     ...
01433     vigra::linearNoiseNormalization(srcImageRange(src), destImage(dest), 
01434                                     vigra::NoiseNormalizationOptions().windowRadius(9).noiseVarianceInitialGuess(25.0).
01435                                     clusterCount(15));
01436     \endcode
01437 
01438     <b> Required Interface:</b>
01439     
01440     same as \ref noiseVarianceEstimation()
01441 */
01442 doxygen_overloaded_function(template <...> bool linearNoiseNormalization)
01443 
01444 template <class SrcIterator, class SrcAccessor,
01445           class DestIterator, class DestAccessor>
01446 inline bool
01447 linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01448                                 DestIterator dul, DestAccessor dest,
01449                                 NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01450 {
01451     typedef typename SrcAccessor::value_type SrcType;
01452 
01453     return detail::linearNoiseNormalizationImpl(sul, slr, src, dul, dest, options,
01454                                          typename NumericTraits<SrcType>::isScalar());
01455 }
01456 
01457 template <class SrcIterator, class SrcAccessor,
01458           class DestIterator, class DestAccessor>
01459 inline
01460 bool linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01461                                      pair<DestIterator, DestAccessor> dest,
01462                                      NoiseNormalizationOptions const & options = NoiseNormalizationOptions())
01463 {
01464     return linearNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, options);
01465 }
01466 
01467 /********************************************************/
01468 /*                                                      */
01469 /*                 linearNoiseNormalization             */
01470 /*                                                      */
01471 /********************************************************/
01472 
01473 /** \brief Noise normalization by means of a given linear noise model.
01474 
01475     This function works similar to \ref nonparametricNoiseNormalization() with the exception that the 
01476     functional dependency of the noise variance from the intensity is given (as a linear function) 
01477     rather than estimated:
01478     
01479     \code
01480     variance = a0 + a1 * intensity
01481     \endcode
01482     
01483     <b> Declarations:</b>
01484     
01485     pass arguments explicitly:
01486     \code
01487     namespace vigra {
01488         template <class SrcIterator, class SrcAccessor,
01489                   class DestIterator, class DestAccessor>
01490         void linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01491                                       DestIterator dul, DestAccessor dest,
01492                                       double a0, double a1);
01493     }
01494     \endcode
01495     
01496     use argument objects in conjunction with \ref ArgumentObjectFactories :
01497     \code
01498     namespace vigra {
01499         template <class SrcIterator, class SrcAccessor,
01500                   class DestIterator, class DestAccessor>
01501         void linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01502                                       pair<DestIterator, DestAccessor> dest,
01503                                       double a0, double a1);
01504     }
01505     \endcode
01506     
01507     <b> Usage:</b>
01508     
01509         <b>\#include</b> <<a href="noise__normalization_8hxx-source.html">vigra/noise_normalization.hxx</a>><br>
01510     Namespace: vigra
01511     
01512     \code
01513     vigra::BRGBImage src(w,h), dest(w, h);
01514     
01515     ...
01516     vigra::linearNoiseNormalization(srcImageRange(src), destImage(dest), 
01517                                     100, 0.02);
01518     \endcode
01519 
01520     <b> Required Interface:</b>
01521     
01522     The source value type must be convertible to <tt>double</tt> or must be a vector whose elements 
01523     are convertible to <tt>double</tt>. Likewise, the destination type must be assignable from <tt>double</tt>
01524     or a vector whose elements are assignable from <tt>double</tt>.
01525 */
01526 doxygen_overloaded_function(template <...> void linearNoiseNormalization)
01527 
01528 template <class SrcIterator, class SrcAccessor,
01529           class DestIterator, class DestAccessor>
01530 inline
01531 void linearNoiseNormalization(SrcIterator sul, SrcIterator slr, SrcAccessor src,
01532                               DestIterator dul, DestAccessor dest,
01533                               double a0, double a1)
01534 {
01535     typedef typename SrcAccessor::value_type SrcType;
01536 
01537     detail::linearNoiseNormalizationImpl(sul, slr, src, dul, dest, a0, a1,
01538                                          typename NumericTraits<SrcType>::isScalar());
01539 }
01540 
01541 template <class SrcIterator, class SrcAccessor,
01542           class DestIterator, class DestAccessor>
01543 inline
01544 void linearNoiseNormalization(triple<SrcIterator, SrcIterator, SrcAccessor> src,
01545                               pair<DestIterator, DestAccessor> dest,
01546                               double a0, double a1)
01547 {
01548     linearNoiseNormalization(src.first, src.second, src.third, dest.first, dest.second, a0, a1);
01549 }
01550 
01551 //@}
01552 
01553 } // namespace vigra
01554 
01555 #endif // VIGRA_NOISE_NORMALIZATION_HXX

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de)
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

html generated using doxygen and Python
vigra 1.7.1 (Sun Feb 19 2012)